We have shown (Younes & Simmons, 2002) that
giving priority to static open conditions can be beneficial when
planning with lifted actions. Introducing a new flaw type, ``t'',
representing static open conditions, we can specify this flaw
selection strategy as follows:

Static-First {t}LIFO / {n, s}LIFO / {o}LIFO

A static open condition is a literal that involves a predicate
occurring in the initial conditions of a planning problem, but not in
the effects of any operator in the planning domain. This means that a
static open condition always has to be linked to the initial
conditions, and the initial conditions consist solely of ground
literals. Resolving a static open condition
ai will
therefore cause all free variables of q to be bound to specific
objects. Resolving static open conditions before other flaws
represents a bias towards early commitment of parameter bindings.
This resembles the search strategy inherent in planners using ground
actions, but without necessarily committing to bindings for all
parameters of an action at once. The gain is a reduced branching
factor compared to a planner using ground actions, and this reduction
can compensate for the increased complexity that comes with having to
keep track of variable bindings.

Our earlier results (Younes & Simmons, 2002)
indicated that despite a reduction in the number of generated plans
when planning with lifted actions, using ground actions was still
faster in most domains. In the gripper domain, for example, while
using lifted actions resulted in less than half the number of
generated plans compared to when using ground actions, planning with
ground actions was still more than twice as fast. We have greatly
improved the implementation of the planner and the handling of
variable bindings since then. When using the latest version of
VHPOP in the gripper domain, the planner is roughly as fast
when planning with lifted actions giving priority to static
preconditions as when planning with ground actions.